Towards targeted exploration for non-stochastic disturbances

Conference item

We present a novel targeted exploration strategy for linear time-invariant systems without stochastic assumptions on the noise, i.e., without requiring independence or zero mean, allowing for deterministic model misspecifications. This work utilizes classical data-dependent uncertainty bounds on the least-squares parameter estimates in the presence of energy-bounded noise. We provide a sufficient condition on the exploration data that ensures a desired error bound on the estimated parameter. Using common approximations, we derive a semidefinite program to compute the optimal sinusoidal input excitation. Finally, we highlight the differences and commonalities between the developed non-stochastic targeted exploration strategy and conventional exploration strategies based on classical identification bounds through a numerical example.

Authors: Venkatasubramanian, J; Koehler, J; Cannon, M; Allgoewer, F

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: IFAC-PapersOnLine
• Volume: 58
• Issue: 15
• Pages: 556-561
• Publication date: 2024-09-19
• Acceptance date: 2024-05-15
• Event title: 19th IFAC Symposium on System Identification (SYSID 2024)
• Event location: Boston, MA, USA
• Event website: https://conferences.ifac-control.org/sysid2024/
• Event start date: 2024-07-17
• Event end date: 2024-07-18
• DOI: 10.1016/j.ifacol.2024.08.588
• EISSN: 2405-8963
• ISSN: 2405-8971
• Language: English
• Keywords: identification for control; uncertainty quantification; experiment design